AsteroidsNew algorithm for asteroid detection (GitHub repo is currently private, contact me for details). | |
| Project: | We want to detect potentially hazardous asteroids with the upcoming Large Synoptic Survey Telescope. |
| Technologies: | Python |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Jes Ford @ University of Washington |
BenchmarkInstanceCatalogsBenchmark instance catalogs in LSST catalog simulations | |
| Project: | LSST Catalog Simulations |
| Technologies: | |
| GitHub: | https://GitHub.com/rbiswas4/BenchmarkInstanceCatalogs |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
CadenceBuild alternative observing strategies ignoring scheduling constraints and evaluate them | |
| Project: | LSST |
| Technologies: | |
| GitHub: | https://GitHub.com/rbiswas4/FluctuationsInCosmology |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
Cluster Mass Function with neutrinosStudy cluster mass functions from HACC simulations | |
| Project: | Large Scale Structure |
| Technologies: | |
| GitHub: | https://GitHub.com/rbiswas4/FluctuationsInCosmology |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
Detect repetitive seismic signals using AutocorrelationDevelop an effective autocorrelation technique that is capable of detecting repetitive seismic signals (e.g., earthquakes, volcanic and tectonic tremors) through multi-year-long waveforms recorded by Pacific Northwest Seismic Network (PNSN). | |
| Project: | |
| Technologies: | GPU, Shell, Python |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Xiaofeng Meng (xmeng@uw.edu) |
Diffusion MRI in Python (DIPY)A software library for analysis of diffusion MRI data. | |
| Project: | Multi-dimensional analysis of tracts (with Jason Yeatman, UW ILABS, and Noah Simon, UW Biostats) |
| Technologies: | Python |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | arokem@gmail.com |
Exploring Systematic Effects in SDSS Galaxy Spectral DataWe analyze potential systematic errors in the observed relationship between stellar mass, gas phase metallicity, and star formation rate. All of these quantities are derived from measurements of galaxy spectra using many implicit assumptions, leading to the possibility of biases in these parameters that are correlated with each other. Since this observed relationship is used to constrain models of fundamental processes governing galaxy evolution (gas inflow/outflow, star formation, metal enrichment), it is crucial to understand how systematics may affect the strength of the correlations between these galaxy properties. GitHub link & paper preprint coming soon! | |
| Project: | http://staff.washington.edu/otelford/research.html (to be updated soon!) |
| Technologies: | Python |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Grace Telford, Julianne Dalcanton |
Galaxy ExplorerA project for the CSE 512 Data Visualization class (Spring 2015). This tool facilitates interactive exploration of correlations between parameters in a high-dimensional astronomy dataset. This dataset contains measurements of 25 properties of 10,000 galaxies derived from spectra from the Sloan Digital Sky Survey. Our web-based tool, implemented using D3 and JavaScript, allows the user to rapidly generate one and two-dimensional orthogonal projections of the dataset and dynamically change these projections. It also enables brushing and linking between plots so that the user can search for variations in the distribution of galaxies in different regions of parameter space. https://GitHub.com/CSE512-15S/fp-otelford-mahirk-athern | |
| Project: | http://cse512-15s.GitHub.io/fp-otelford-mahirk-athern/ |
| Technologies: | D3, JavaScript |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Grace Telford, Nicole Atherly, Mahir Kothary |
Generalized Linear Models for Neural EncodingModules in Python for fitting GLM models | |
| Project: | The modules aim to incorporate different basis functions. |
| Technologies: | Python |
| GitHub: | https://GitHub.com/valentina-s/GLM_PythonModules |
| Zenodo: | |
| Link: | |
| Contact: | Valentina Staneva |
HYDRAExtracting data from calcium image sequences in a behaving organism. Organism is moving and warping in space. But, camera is fixed in space. Calcium markers temporally switch on and off | |
| Project: | Reverse engineering Hydra Vulgaris. Calcium image sequences of Hydra with calcium markers in neural cells. Extract spike trains. Reverse engineer Hydra nervous system from spike trains. |
| Technologies: | TIFF, openCV, Matlab, QT, optical flow, hierarchical markov models, particle filter |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Amnon Horowitz, amnonh@uw.edu |
LSST SimulationsCatalog simulations for LSST | |
| Project: | LSST catalog simulations |
| Technologies: | Python |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | http://lsst.org/scientists/simulations |
LSST_PipeLineProject to build simulations, analysis of supernoave observations from the observing Strategy | |
| Project: | supernova cosmology |
| Technologies: | Python, workflow tools |
| GitHub: | https://GitHub.com/rbiswas4/lsstpipeline |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
Metabolic model-based integration of taxonomic and metabolomic data from microbial communitiesA simple modeling framework to characterize relationships between taxonomic composition and metabolite abundances in microbial community samples. Code available at http://elbo.gs.washington.edu/download.html | |
| Project: | See elbo.gs.washington.edu/research.html, publication forthcoming in mSystems |
| Technologies: | R |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Cecilia Noecker (cnoecker@uw.edu) |
Microbial Remineralization Model v1.0The Microbial Remineralization Model v1.0 simulates the interactions between sinking particles and heterotrophic bacteria in the ocean water column in a 1-dimensional Eulerian framework. The model has 9 state variables including particulate organic carbon, particle-attached bacteria, free-living bacteria, active exoenzyme in the particle, inactive exoenzyme in the particle, hydrolysate in the particle, hydrolysate in the dissolved environment, active exoenzyme in the dissolved environment, and inactive exoenzyme in the dissolved environment. | |
| Project: | Mislan, K. A. S., C. A. Stock, J. P. Dunne, and J. L. Sarmiento. 2014. Group behavior among model bacteria influences particulate carbon remineralization depths. Journal of Marine Research. 72:183-218 http://dx.doi.org/10.1357/002224014814901985 |
| Technologies: | Fortran, R, Shell |
| GitHub: | https://GitHub.com/kallisons/MicrobeReminModel_v1.0 |
| Zenodo: | http://dx.doi.org/10.5281/zenodo.16145 |
| Link: | |
| Contact: | K. Allison Smith (www.kallisonsmith.us), Charles Stock (www.gfdl.noaa.gov/charles-stock-homepage) |
Myria Big Data Management System and ServiceMyria is a distributed, shared-nothing Big Data management system and cloud service developed through a collaboration between the UW database group and the eScience Institute. You can use Myria to manage and analyze small to large datasets. Myria enables the analysis of data directly from the browser and also from Python scripts. Myria further supports data analysis across multiple big data systems. Myria runs on Amazon EC2. Users can easily spin up their own Myria clusters on Amazon. Try the demo of the service under the research project link below. | |
| Project: | http://myria.cs.washington.edu/ |
| Technologies: | SQL, Java, Python, Amazon Web Services, Parallel and distributed data management, query optimization, query execution. |
| GitHub: | https://GitHub.com/uwescience/myria-stack |
| Zenodo: | |
| Link: | |
| Contact: | Magdalena Balazinska, Bill Howe, Myria team, University of Washington Database Group . |
NOAH LSM Mussel v2.0NOAH LSM Mussel v2.0 is a mathematical model that predicts mussel bed temperatures from atmospheric and oceanic data by mimicking the thermal properties of a mussel bed exposed to tidal inundation and wave run-up. The model is derived from the National Weather Service NOAH Land Surface Model. In v2.0, it is possible to change the within mussel bed contact which determines conductive heat transfer. Mussel survival is predicted using mussel bed temperatures from the model. | |
| Project: | Mislan, K. A. S. and Wethey, D. S. (2015). A biophysical basis for patchy mortality during heat waves. Ecology, 96:902-907 http://dx.doi.org/10.1111/geb.12160 |
| Technologies: | Fortran, R, Shell |
| GitHub: | https://GitHub.com/kallisons/NOAH_LSM_Mussel_v2.0 |
| Zenodo: | http://dx.doi.org/10.5281/zenodo.13380 |
| Link: | |
| Contact: | K. Allison Smith (www.kallisonsmith.us), David Wethey (ww2.biol.sc.edu/~wethey/) |
Neuropill Modeling in Calcium ImagingAutomatic extraction and removal of neuropill signals from sequence of calcium fluorescence data of neural activity | |
| Project: | This project attempt dimensionality reduction techniques to distinguish neuropill signal from noise in the images. |
| Technologies: | Python |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Valentina Staneva |
OpSimSummaryProject to study the Operation Simulations Output to study observation strategies for LSST | |
| Project: | LSST |
| Technologies: | Python |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
P50 Depth Analysis v1.0The P50 Depth Analysis v1.0 calculates blood-oxygen binding, which is a mechanism determining hypoxia tolerance in the ocean. Blood-oxygen binding is measured as the oxygen pressure in the blood at which whole blood is 50% oxygenated, called P50. A low P50 means that respiratory pigments in the blood of an organism equilibrate to 100% oxygenation at lower oxygen pressures, and the organism is more hypoxia tolerant. Temperature alters hypoxia tolerance by shifting the P50 of organisms. The effect of temperature is measured as the heat of oxygenation (ΔH) which is the amount of heat energy released (negative ΔH) or absorbed (positive ΔH) when oxygen binds to respiratory pigments. Marine organisms swim between warmer, well-oxygenated waters near the surface of the ocean and colder, less-oxygenated waters in the deeper ocean. This analysis assumes P50 acclimation to temperatures at 10 m depth in the mixed layer. P50 in the water column can be determined relative to P50 at 10 m depth using the Van’t Hoff equation and oxygen pressure and temperature data. The P50 depth is defined as the shallowest depth in the ocean where pO2 = P50. This code calculates P50 depths for the global ocean. | |
| Project: | Mislan, K. A. S., J. P. Dunne, and J. L. Sarmiento. (2015) The fundamental niche of blood-oxygen binding in the pelagic ocean. Oikos. http://dx.doi.org/10.1111/oik.02650 |
| Technologies: | R, Shell, Python |
| GitHub: | https://GitHub.com/kallisons/P50DepthAnalysis_v1.0 |
| Zenodo: | http://dx.doi.org/10.5281/zenodo.31951 |
| Link: | |
| Contact: | K. Allison Smith (www.kallisonsmith.us) |
RAMBUTANPrediction of the 3D structure of the human genome from sequence-level information. The three dimensional structure of the human genome is known to be cell-type specific. This structure is defined by a set of pairwise contacts which can be experimentally identified using Hi-C, which is both time intensive and expensive. If we can build a model which bypasses the need to run Hi-C, we can significantly improve the speed at which structural information is acquired. | |
| Project: | Build a model which predicts whether or not two 1kb regions of the genome are in contact with each other, using only nucleotide sequence and nucleotide-level DNaseI hypersensitive. |
| Technologies: | Deep learning, convolutional neural networks, Python, cython, Hi-C, DNase |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Jacob Schreiber |
SNCosmoPython library for supernova cosmology | |
| Project: | Supernova Cosmology |
| Technologies: | Python |
| GitHub: | https://GitHub.com/sncosmo/sncosmo |
| Zenodo: | |
| Link: | |
| Contact: | Kyle Barbary |
SNPGMInferencing Cosmological parameters from SALT2 light curve models | |
| Project: | |
| Technologies: | Python, sampling |
| GitHub: | https://GitHub.com/rbiswas4/snpgm |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
SNSimsGeneration of mixed SNIa and SNcc light Curves generated with different methods | |
| Project: | supernova cosmology |
| Technologies: | Python |
| GitHub: | https://GitHub.com/rbiswas4/SNsims |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
ShablonaA template for small scientific Python projects | |
| Project: | |
| Technologies: | |
| GitHub: | |
| Zenodo: | |
| Link: | https://GitHub.com/uwescience/shablona |
| Contact: | |
Simulating Circumbinary Planet HabitabilityThe code VPLANET combines physics ranging from star – planet tidal evolution to plate tectonics to planetary atmospheric escape to be the most accurate means to simulate the full dynamical evolution of a planetary system. With so much physics to incorporate to accurately model a circumbinary planet’s potential habitability, of order 100 physical parameters must be explored over potentially billions of years of planetary evolution. To study and analyze this vast parameter space, in the future I will employ machine learning techniques to a massive ensemble of simulations to shed new light on the dynamical history and potential habitability of circumbinary planets. | |
| Project: | https://GitHub.com/dflemin3 |
| Technologies: | C, Python, Machine Learning |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | dflemin3@uw.edu / Rory Barnes, David Fleming |
Simulating nonlinear harmonics for lithium ion batteriesTool for taking coupled, nonlinear PDEs for modeling battery physics and solves for the resulting steady periodic harmonics using a spectral method | |
| Project: | Using harmonics as battery health diagnostics |
| Technologies: | COMSOL, Matlab, Mathematica |
| GitHub: | |
| Zenodo: | |
| Link: | |
| Contact: | Matt Murbach |
Visualizing relationships between taxonomic and functional abundances in microbial communitiesStarted as a CSE512 final project. We are building an interactive tool for exploratory analysis of microbiome datasets describing both taxonomic and functional or predicted functional composition. Our tool visualizes the relationships between these data types and their variation across samples. | |
| Project: | see GitHub repo |
| Technologies: | D3, Javascript |
| GitHub: | http://GitHub.com/borenstein-lab/burrito |
| Zenodo: | |
| Link: | |
| Contact: | Cecilia Noecker (cnoecker@uw.edu), Alex Eng (engal@uw.edu), Colin McNally (cmcn@uw.edu), help from Will Gagne-Maynard (gagnemaw@uw.edu) |
analyzeSNAnalysis of SN light Curves generated with different methods | |
| Project: | Supernova Cosmology |
| Technologies: | Python |
| GitHub: | https://GitHub.com/rbiswas4/sncosmo_lc_analysis |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
cluster-lensingPython code for modeling galaxy cluster weak lensing profiles. | |
| Project: | An earlier version of this code was used for the following weak gravitational lensing studies |
| Technologies: | Python, C |
| GitHub: | https://GitHub.com/jesford/cluster-lensing |
| Zenodo: | |
| Link: | |
| Contact: | Jes Ford @ University of Washington |
cluster_analysisShiny based web app with tools for expression based analyses of microorganisms clustering regulatory motif discovery sequence search exploratory analysis workflow capture | |
| Project: | http://faculty.washington.edu/dacb/ |
| Technologies: | R, Shiny |
| GitHub: | https://GitHub.com/dacb/cluster_analysis |
| Zenodo: | |
| Link: | |
| Contact: | http://faculty.washington.edu/dacb/ |
hostExtinctionProject to study the impact of host extinction | |
| Project: | Supernova Cosmology |
| Technologies: | Python |
| GitHub: | private |
| Zenodo: | |
| Link: | |
| Contact: | Rahul Biswas |
msmskitMS/MS analysis kit – a distributed and parallel database centric pipeline for analysis of MS2 data with SEQUEST/COMET and the *Prophet family | |
| Project: | |
| Technologies: | Bash, SQL, R |
| GitHub: | https://GitHub.com/dacb/msmskit |
| Zenodo: | |
| Link: | |
| Contact: | http://faculty.washington.edu/dacb/ |
